FinGPT README

The project's README from github.com/AI4Finance-Foundation/FinGPT. As one markdown file: README.md.

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FinGPT: Open-Source Financial Large Language Models

Downloads Downloads Join Discord Python 3.8 PyPI License

Let us not expect Wall Street to open-source LLMs or open APIs, due to FinTech institutes' internal regulations and policies.

Blueprint of FinGPT

https://huggingface.co/FinGPT

Visitors Discord

Project Contributors

FinGPT is an open-source financial large language model project developed and maintained by the AI4Finance Foundation.

Key contributors include:

What's New:

Quick Start

🚀 Get started with FinGPT in minutes!

For detailed setup instructions for running FinGPT locally or on Replit, including hardware requirements and troubleshooting, check out our comprehensive SETUP Guide.

Installation

# Clone the repository
git clone https://github.com/AI4Finance-Foundation/FinGPT.git
cd FinGPT

# Install dependencies
pip install -r requirements.txt
pip install -e .

Try the Demo

Visit our HuggingFace Space to try FinGPT-Forecaster without any installation!

Basic Usage

# Using cloud API (no GPU required)
import os
os.environ['FINGPT_LLM_PROVIDER'] = 'openai'

# Using local models (requires GPU)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# See SETUP.md for detailed examples

Why FinGPT?

1). Finance is highly dynamic. BloombergGPT trained an LLM using a mixture of finance data and general-purpose data, which took about 53 days, at a cost of around $3M). It is costly to retrain an LLM model like BloombergGPT every month or every week, thus lightweight adaptation is highly favorable. FinGPT can be fine-tuned swiftly to incorporate new data (the cost falls significantly, less than $300 per fine-tuning).

2). Democratizing Internet-scale financial data is critical, say allowing timely updates of the model (monthly or weekly updates) using an automatic data curation pipeline. BloombergGPT has privileged data access and APIs, while FinGPT presents a more accessible alternative. It prioritizes lightweight adaptation, leveraging the best available open-source LLMs.

3). The key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables an LLM model to learn individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.), which is the "secret" ingredient of ChatGPT and GPT4.

Milestone of AI Robo-Advisor: FinGPT-Forecaster

Try the latest released FinGPT-Forecaster demo at our HuggingFace Space

The dataset for FinGPT-Forecaster: https://huggingface.co/datasets/FinGPT/fingpt-forecaster-dow30-202305-202405

demo_interface

Enter the following inputs:

  1. ticker symbol (e.g. AAPL, MSFT, NVDA)
  2. the day from which you want the prediction to happen (yyyy-mm-dd)
  3. the number of past weeks where market news are retrieved
  4. whether to add the latest basic financials as additional information

Click Submit！ And you'll be responded with a well-rounded analysis of the company and a prediction for next week's stock price movement!

For detailed and more customized implementation, please refer to FinGPT-Forecaster

FinGPT Demos:

Current State-of-the-arts for Financial Sentiment Analysis

Instruction Tuning Datasets and Models

The datasets we used, and the multi-task financial LLM models are available at https://huggingface.co/FinGPT

Our Code

Datasets Train Rows Test Rows Description
fingpt-sentiment-train 76.8K N/A Sentiment Analysis Training Instructions
fingpt-finred 27.6k 5.11k Financial Relation Extraction Instructions
fingpt-headline 82.2k 20.5k Financial Headline Analysis Instructions
fingpt-ner 511 98 Financial Named-Entity Recognition Instructions
fingpt-fiqa_qa 17.1k N/A Financial Q&A Instructions
fingpt-fineval 1.06k 265 Chinese Multiple-Choice Questions Instructions

Multi-task financial LLMs Models:

  demo_tasks = [
      'Financial Sentiment Analysis',
      'Financial Relation Extraction',
      'Financial Headline Classification',
      'Financial Named Entity Recognition',]
  demo_inputs = [
      "Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano",
      "Apple Inc. Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.",
      'gold trades in red in early trade; eyes near-term range at rs 28,300-28,600',
      'This LOAN AND SECURITY AGREEMENT dated January 27 , 1999 , between SILICON VALLEY BANK (" Bank "), a California - chartered bank with its principal place of business at 3003 Tasman Drive , Santa Clara , California 95054 with a loan production office located at 40 William St ., Ste .',]
  demo_instructions = [
      'What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.',
      'Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be "relation1: word1, word2; relation2: word3, word4". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.',
      'Does the news headline talk about price going up? Please choose an answer from {Yes/No}.',
      'Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.',]
Models Description Function
fingpt-mt_llama2-7b_lora Fine-tuned Llama2-7b model with LoRA Multi-Task
fingpt-mt_falcon-7b_lora Fine-tuned falcon-7b model with LoRA Multi-Task
fingpt-mt_bloom-7b1_lora Fine-tuned bloom-7b1 model with LoRA Multi-Task
fingpt-mt_mpt-7b_lora Fine-tuned mpt-7b model with LoRA Multi-Task
fingpt-mt_chatglm2-6b_lora Fine-tuned chatglm-6b model with LoRA Multi-Task
fingpt-mt_qwen-7b_lora Fine-tuned qwen-7b model with LoRA Multi-Task
fingpt-sentiment_llama2-13b_lora Fine-tuned llama2-13b model with LoRA Single-Task
fingpt-forecaster_dow30_llama2-7b_lora Fine-tuned llama2-7b model with LoRA Single-Task

Tutorials

[Training] Beginner’s Guide to FinGPT: Training with LoRA and ChatGLM2–6B One Notebook, $10 GPU

Understanding FinGPT: An Educational Blog Series

FinGPT Ecosystem

FinGPT embraces a full-stack framework for FinLLMs with five layers:

  1. Data source layer: This layer assures comprehensive market coverage, addressing the temporal sensitivity of financial data through real-time information capture.
  2. Data engineering layer: Primed for real-time NLP data processing, this layer tackles the inherent challenges of high temporal sensitivity and low signal-to-noise ratio in financial data.
  3. LLMs layer: Focusing on a range of fine-tuning methodologies such as LoRA, this layer mitigates the highly dynamic nature of financial data, ensuring the model’s relevance and accuracy.
  4. Task layer: This layer is responsible for executing fundamental tasks. These tasks serve as the benchmarks for performance evaluations and cross-comparisons in the realm of FinLLMs
  5. Application layer: Showcasing practical applications and demos, this layer highlights the potential capability of FinGPT in the financial sector.

Cloud LLM Providers for FinGPT Inference

For the Finogrid platform (finogrid/), FinGPT supports multiple cloud LLM providers as alternatives to running local models. Set FINGPT_LLM_PROVIDER in your environment:

Provider Model Context Length Use Case
OpenAI GPT-3.5-turbo 16K Default fallback for sentiment & agents
MiniMax MiniMax-M3 512K Latest flagship model with 128K max output and image input support
Fin

FinGPT (AI4Finance-Foundation/FinGPT)

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21.4K ·GitHub
FinGPT
Что это такое?FinGPT — это open-source проект фонда AI4Finance Foundation, посвящённый финансовым большим языковым моделям: обученные модели выложены на HuggingFace, а вокруг них — код для RAG, пайплайны дообучения и бенчмарки.
FinGPT — это одна модель или целый набор инструментов?Скорее набор компонентов, чем одна модель: FinGPT-RAG (фреймворк с поиском по базе знаний для анализа тональности финансовых текстов), FinGPT-FinNLP (пайплайны обучения и дообучения) и FinGPT-Benchmark (оценка качества).
Где лежат обученные модели и как начать ими пользоваться?Обученные модели опубликованы на HuggingFace и готовы к использованию сразу, либо можно пройтись по пайплайнам FinGPT-FinNLP, которые покрывают полное обучение и дообучение LLM для финансов; подробности описаны в вики проекта.
Что должно быть установлено, чтобы вообще что-то запустить?Нужна среда Python — репозиторий состоит из кода на Python и Jupyter Notebook, без Docker, так что пайплайны и ноутбуки вы запускаете сами на своей машине.
Это готовый продукт или всё ещё исследовательский код?Это исследовательский код, а не законченное приложение: вас ждут ноутбуки, пайплайны и бенчмарки, которые нужно настраивать самому, без упакованного установщика, а для локального обучения всё ещё нужно собственное железо.
Мне нужно разбираться в финансах с Уолл-стрит, чтобы этим пользоваться?Нет: часть FinNLP прямо задумана как площадка для всех, кто интересуется LLM и NLP в финансах — исследователей, разработчиков и энтузиастов, изучающих применение языковых моделей в финансовых задачах.
Мне нужна своя видеокарта (GPU), чтобы запустить FinGPT?Не обязательно: на платформе Finogrid можно выставить FINGPTLLMPROVIDER на облачного провайдера вместо локальных моделей — OpenAI GPT-3.5-turbo (16K) служит запасным вариантом по умолчанию для анализа тональности и агентов, а MiniMax-M3 даёт контекст 512K.
А зачем вообще нужны open-source финансовые LLM?Проект исходит из того, что Уолл-стрит не станет выпускать открытые LLM или открытые API из-за внутренних регламентов и политик финтех-институтов — FinGPT существует как открытая альтернатива, на которой может строить что угодно каждый.
Как на самом деле измеряется качество моделей?Через FinGPT-Benchmark: это новый подход к instruction-tuning для open-source LLM в финансах, поддерживающий настройку под конкретные задачи, мультитасковую и zero-shot, чтобы бенчмаркинг на разных финансовых датасетах оставался систематичным и недорогим.
Насколько популярен FinGPT?Очень: около 21.4k звёзд на GitHub, поддерживается фондом AI4Finance Foundation, а обученные модели выложены на HuggingFace для прямого скачивания.
What are similar projects?jeeves, airllm, verl, MiniCPM, train-llm-from-scratch, stanford_alpaca, RD-Agent, llm-course
Which topics is it in?financial-analysis, technical-analysis, instruction-tuning, sentiment-analysis, quantitative-finance, fintech, training, finance, reinforcement-learning, jupyter notebook, fine-tuning, chatgpt
Creator
AI4Finance-Foundation
Size
12.9 MB
Stars
21.4K
Updated
CommitSep 23Releasev1.0.0Apr 08
Age
3y
Commits · 90 Days